Upstream oil and gas industry services work to deliver success throughout the life cycle of the reservoir. However, conventional sources of oil and gas are declining; therefore operators are increasingly turning their attention to unexplored and underdeveloped areas. This includes high pressure/temperature and deepwater areas, as well as working to increase recoveries in mature fields. As reservoirs become more complex and drilling operations become more expensive, there is a growing need to reduce inefficiencies and costs. Petroleum engineers are increasingly using optimized formation evaluation techniques, software with three-dimensional visualization, and multi-disciplinary data interpretation techniques. In addition, data from new downhole equipment provides reliable, real-time information about downhole conditions. With these improved techniques and better data, operators can model, predict, and control their operations better in real-time, thereby reducing inefficiencies and cost. However, this massive integration of varied data moving in higher volumes and at increased speeds has put an increasing demand on the computation and use of actionable and predictive data-driven analytics. Furthermore, these analytics must work in real-time so as to quickly discover the important critical data attributes and features for use in forecasting.
Attribute importance is a well-known statistical technique used to identify critical attributes and features within a set of attributes that could impact a specific target. Various standard and custom techniques—each having their own strengths and weaknesses—are available for performing attribute importance. Each technique applies a different function to evaluate the importance of an attribute and produces a ranked subset of attributes. Therefore, it is possible to arrive at different subsets of important attributes based on the choice of the various attribute importance techniques.
Attribute importance techniques have been successfully applied to a wide range of problems in the oil and gas space including drilling, production, reservoir simulation and seismic analysis. In data mining, attribute importance techniques can be used to input parameters for other types of data mining algorithms or discover knowledge by itself. In this latter task, the rules that such techniques found are usually general because of each techniques global search and rank nature. In contrast, most other data mining methods are based on the rule induction paradigm, where the algorithm usually performs a type of local search. The advantage of such techniques becomes more obvious when the attribute space of a task is unmanageably large. While use of multiple statistical techniques to perform attribute importance is common and valuable, deriving a final set of ranked important attributes that accommodates the output of such techniques is a time consuming, manual and error prone task.